Face annotation method and a face annotation system

Abstract

A face annotation method and a face annotation system adapted for a current owner to annotate contacts in online social networks (OSNs) are provided. The face annotation method includes: providing a Hierarchical Database Access (HDA) architecture for each member according to various social network contexts; providing a Fused Face Recognition (FFR) unit which uses an Adaboost algorithm to fuse a plurality types of base classifiers; and constructing a Face Recognizer by integrating the HDA architecture and the corresponding FFR unit for the owner and each member via respective types of the social network contexts; and selecting suitable personalized Face Recognizers and merging multiple personalized Face Recognizer results by using at least two collaborative FR strategies.

What is claimed is: 
     
       1. A face annotation method, adapted for an owner to annotate members in online social networks (OSNs), comprising:
 providing a Hierarchical Database Access (HDA) architecture for each member according to various social network contexts; 
 providing a Fused Face Recognition (FFR) unit which uses an Adaboost algorithm to fuse a plurality types of base classifiers; and 
 constructing a Face Recognizer by integrating the HDA architecture and the corresponding FFR unit for the owner and each member via respective types of the social network contexts; and 
 selecting suitable personalized Face Recognizers and merging multiple personalized Face Recognizer results by using at least two collaborative FR strategies. 
 
     
     
       2. The face annotation method as recited in  claim 1 , wherein the social network contexts include a recurrence context, a temporal context, and a group context. 
     
     
       3. The face annotation method as recited in  claim 2 , wherein the HDA architecture includes four layers of database, wherein a first layer is based on the recurrence context, a second layer is based on the temporal context or the temporal-group context, a third layer is based on the group context, and a fourth layer contains all face images of the members and the owner. 
     
     
       4. The face annotation method as recited in  claim 1 , wherein the FFR unit includes a training process and a testing process,
 wherein the training process trains each base classifier using tagged face images obtained from photo collections of the owner shared on the OSNs, and the testing process uses weights trained from the training process to generate an AdaBoost result score of an unlabeled query face image. 
 
     
     
       5. The face annotation method as recited in  claim 4 , wherein the training process comprises:
 initializing weights of face images based on the Adaboost algorithm; 
 calculating an error rate of each of the base classifier; and 
 after the weights of the base classifier is calculated, each of the base classifiers is iteratively boosted by updating the weights of samples during each round. 
 
     
     
       6. The face annotation method as recited in  claim 5 , wherein the testing process comprises:
 calculating the AdaBoost result score using a linear combination of base classifiers with the updating weights. 
 
     
     
       7. The face annotation method as recited in  claim 1 , wherein the step of constructing the Face Recognizer by integrating the HDA architecture and the corresponding FFR unit comprises:
 accessing a level of the HDA architecture with an query face image in order of priority according to the social network contexts; 
 if a face recognition score of the query face image is higher than a face recognition threshold, the query face image is tagged; and 
 if the face recognition score of the query face image is lower than the face recognition threshold when the lowest priority of the social network contexts is accessed, the query face image is regarded as unidentified. 
 
     
     
       8. The face annotation method as recited in  claim 1 , wherein the collaborative FR strategies includes employing the owner with a Highest Priority Rule and utilizing a Weighted Majority Rule,
 wherein the step of employing the owner with the Highest Priority Rule comprises: 
 when the owner uploads unlabelled query face images onto the OSN, the Face Recognizer of the owner with the highest priority annotates identity labels of the query face images, and the Face Recognizers selected from the members recognizes unidentified query face images, 
 wherein the step of utilizing the Weighted Majority Rule comprises: 
 selecting and merging the Face Recognizers of the owner and the members, and annotating the identity labels of the query face images according to scores calculated from the merged Face Recognizers. 
 
     
     
       9. A face annotation system, adapted for an owner to annotate members in online social networks (OSNs), the face annotation system comprises:
 a Hierarchical Database Access (HDA) architecture, configured for each member according to various social network contexts; 
 a Fused Face Recognition (FFR) unit, configured for using an Adaboost algorithm to fuse a plurality types of base classifiers; and 
 a collaborative face recognizer, including a plurality of Face Recognizers, wherein each of the Face Recognizers is constructed by integrating the HDA architecture and the corresponding FFR unit for the owner and each member via respective types of the social network contexts, 
 wherein the collaborative face recognizer selects suitable personalized Face Recognizers and merges multiple personalized Face Recognizer results by using at least two collaborative FR strategies. 
 
     
     
       10. The face annotation system as recited in  claim 9 , wherein the social network contexts include a recurrence context, a temporal context, and a group context. 
     
     
       11. The face annotation system as recited in  claim 10 , wherein the HDA architecture includes four layers of database, wherein a first layer is based on the recurrence context, a second layer is based on the temporal context or the temporal-group context, a third layer is based on the group context, and a fourth layer contains all face images of the members and the owner. 
     
     
       12. The face annotation system as recited in  claim 9 , wherein the FFR unit includes a training process and a testing process,
 wherein in the training process, the FFR unit trains each base classifier using tagged face images obtained from photo collections of the owner shared on the OSNs, and in the testing process, the FFR unit uses weights trained from the training process to generate an AdaBoost result score of an unlabelled query face image. 
 
     
     
       13. The face annotation system as recited in  claim 12 , wherein in the training, the FFR unit initializes weights of face images based on the Adaboost algorithm and calculates an error rate of each of the base classifier, and after the weights of the base classifier is calculated, each of the base classifiers is iteratively boosted by updating the weights of samples during each round. 
     
     
       14. The face annotation system as recited in  claim 12 , wherein in the testing process, the FFR unit calculates the AdaBoost result score using a linear combination of base classifiers with the updating weights. 
     
     
       15. The face annotation system as recited in  claim 9 , wherein the Face Recognizers accesses a level of the HDA architecture with an query face image in order of priority according to the social network contexts, if a face recognition score of the query face image is higher than a face recognition threshold, the Face Recognizers tags the query face image, and if the face recognition score of the query face image is lower than the face recognition threshold when the lowest priority of the social network contexts is accessed, the Face Recognizers regards the query face image as unidentified. 
     
     
       16. The face annotation system as recited in  claim 9 , wherein the collaborative FR strategies includes employing the owner with a Highest Priority Rule and utilizing a Weighted Majority Rule, and
 in the strategy of employing the owner with the Highest Priority Rule, when the owner uploads unlabelled query face images onto the OSN, the Face Recognizer of the owner with the highest priority annotates identity labels of the query face images, and the collaborative face recognizer uses the Face Recognizers selected from the members to recognize unidentified query face images, and 
 in the strategy of utilizing the Weighted Majority Rule, the collaborative face recognizer selects and merges the Face Recognizers of the owner and the members, and annotates the identity labels of the query face images according to scores calculated from the merged Face Recognizers.